Effects of acute lying and sleep deprivation on metabolic and inflammatory responses of lactating dairy cows.

Dairy cows that are restricted from lying down have a reduced ability to sleep. In other species, sleep loss is a key risk factor for disease, mediated by changes in metabolic and inflammatory responses. The cumulative effect of lying and sleep deprivation on cow health is unknown. The objective was to determine the effects of lying and sleep deprivation on metabolic and inflammatory responses of dairy cows. Data were collected from 8 multiparous and 4 primiparous lactating cows (199 ± 44 d in milk, 77 ± 30 d pregnant; mean ± standard deviation) enrolled in a study using a crossover design. Each cow was exposed to 2 treatments meant to induce sleep loss: (1) human disturbance (imposed by researchers making noise or physical contact when the cow's posture suggested sleep) and (2) lying deprivation (imposed by a wooden grid placed on the pen floor). Cows experienced a 24-h baseline period (d -1) followed by a 24-h treatment period (d 0), with a 12-d washout period between treatments. Baseline and treatment periods were imposed from 2100 to 2059 h. Cows were housed in individual pens during the acclimation period (d -3 and -2), d -1, and d 0. Nonesterified fatty acid and glucose concentrations were measured at 0300, 0900, 1500, and 2059 h on d -1 and 0. Proinflammatory cytokine mRNA [tumor necrosis factor (TNF), interleukin-1B (IL1B), and interleukin-6 (IL6)] abundance in whole-blood leukocytes, both nonstimulated and stimulated with lipopolysaccharide, were assessed at 2059 h on d -1 (end of baseline) and d 0 (end of treatment). Nonesterified fatty acids and glucose varied by time of day but were not affected by treatment or day. The abundances of TNF and IL1B from both stimulated and nonstimulated cells were higher following 24 h of lying deprivation (d 0) compared with baseline (d -1). Abundance of IL6 was increased in nonstimulated cells after lying deprivation compared with baseline. In contrast, human disturbance for 24 h did not alter TNF, IL1B, or IL6 abundance relative to baseline levels. These results suggest that a short period of lying deprivation generally increases inflammatory responses but not metabolic responses.

Development of a noninvasive system for monitoring dairy cattle sleep.

Limited research has been conducted to assess sleep in production livestock primarily because of limitations with monitoring capabilities. Consequently, biological understanding of production circumstances and facility options that affect sleep is limited. The objective of this study was to assess if data collected from a proof-of-concept, noninvasive 3-axis accelerometer device are correlated with sleep and wake-like behaviors in dairy cattle. Four Holstein dairy cows housed at the University of Kentucky Coldstream Dairy in September 2013 were visually observed for 2 consecutive 24-h periods. The accelerometer device was attached to a harness positioned on the right side of each cow's neck. Times of classified behaviors of wake (standing, head up, alert, eyes open) or sleep-like behaviors (lying, still, head resting on ground, eyes closed) were recorded continuously by 2 observers who each watched 2 cows at a time. The radial signal was extracted from 3 different axes of the accelerometer to obtain a motion signal independent of direction of movement. Radial signal features were examined for maximizing the performance of detecting sleep-like behaviors using a Fisher's linear discriminant analysis classifier. The study included 652min of high-activity wake behaviors and 107min of sleep-like behavior among 4 cows. Results from a bootstrapping analysis showed an agreement between human observation and the linear discriminant analysis classifier, with an accuracy of 93.7±0.7% for wake behavior and 92.2±0.8% for sleep-like behavior (±95% confidence interval).This prototype shows promise in measuring sleep-like behaviors. Improvements to both hardware and software should allow more accurate determinations of subtle head movements and respiratory movements that will further improve the assessment of these sleep-like behaviors, including estimates of deep, light, and rapid eye movement sleep. These future studies will require simultaneous electroencephalography and electromyography measures and perhaps additional measures of arousal thresholds to validate this system for measuring true sleep.

Increased fragmentation of sleep-wake cycles in the 5XFAD mouse model of Alzheimer's disease.

Sleep perturbations including fragmented sleep with frequent night-time awakenings and daytime naps are common in patients with Alzheimer's disease (AD), and these daily disruptions are a major factor for institutionalization. The objective of this study was to investigate if sleep-wake patterns are altered in 5XFAD mice, a well-characterized double transgenic mouse model of AD which exhibits an early onset of robust AD pathology and memory deficits. These mice have five distinct human mutations in two genes, the amyloid precursor protein (APP) and Presenilin1 (PS1) engineered into two transgenes driven by a neuron-specific promoter (Thy1), and thus develop severe amyloid deposition by 4 months of age. Age-matched (4-6.5 months old) male and female 5XFAD mice were monitored and compared to wild-type littermate controls for multiple sleep traits using a non-invasive, high throughput, automated piezoelectric system which detects breathing and gross body movements to characterize sleep and wake. Sleep-wake patterns were recorded continuously under baseline conditions (undisturbed) for 3 days and after sleep deprivation of 4h, which in mice produces a significant sleep debt and challenge to sleep homeostasis. Under baseline conditions, 5XFAD mice exhibited shorter bout lengths (14% lower values for males and 26% for females) as compared to controls (p<0.001). In females, the 5XFAD mice also showed 12% less total sleep than WT (p<0.01). Bout length reductions were greater during the night (the active phase for mice) than during the day, which does not model the human condition of disrupted sleep at night (the inactive period). However, the overall decrease in bout length suggests increased fragmentation and disruption in sleep consolidation that may be relevant to human sleep. The 5XFAD mice may serve as a useful model for testing therapeutic strategies to improve sleep consolidation in AD patients.

Tissue classification with generalized spectrum parameters.

This paper presents performance comparisons between breast tumor classifiers based on parameters from a conventional texture analysis (CTA) and the generalized spectrum (GS). The computations of GS-based parameters from radiofrequency (RF) ultrasonic scans and their relationship to underlying scatterer properties are described. Clinical experiments demonstrate classifier performances using 22 benign and 24 malignant breast mass regions taken from 40 patients. Linear classifiers based on parameters from the front edge, back edge and interior tumor regions are examined. Results show significantly better performances for GS-based classifiers, with improvements in empirical receiver operating characteristic (ROC) areas of greater than 10%. The ROC curves show GS-based classifiers achieving a 90% sensitivity level at 50% specificity when applied to the back-edge tumor regions, an 80% sensitivity level at 65% specificity when applied to the front-edge tumor regions, and a 100% sensitivity level at 45% specificity when applied to the interior tumor regions.

A comparative study of transforms for use in digital audio data compression.

This paper provides a subjective quality analysis of transforms used in audio compression algorithms for a class of music signals. A 34-subject listener test compares three transforms in conjunction with an MPEG I layer 1 compression scheme. One test compares the performances of the discrete wavelet packet transform (DWPT) and the modified discrete cosine transform (MDCT) used in MPEG. Another test compares the performances of a DWPT eight-level nonuniform critical-band split and a DWPT five-level uniform subband split. Results indicate that the critical-band split provides significantly better quality than the uniform subband split for sounds with tonal and strong low-frequency content, while the DWPT outperforms the MDCT with significant improvement for nontonal sounds.

Duct detection and wall spacing estimation in breast tissue.

The relationship between duct tissue and several types of malignant disease suggests that methods for characterizing duct structures may be useful tools in ultrasonic tissue characterization. This paper presents performance results from ultrasonic phantom experiments and Monte Carlo simulations for detecting and estimating duct wall spacings on the order of those typically found in breast tissue using methods based on the generalized spectrum (GS) and cepstrum. A performance comparison demonstrates the advantages of each method and examines the effects of various signal processing options, including a special normalization technique for the GS that effectively whitens the data spectrum and reduces interfering spectral influences with little overall performance loss. Experimental results (for both simulation and phantom) indicate that the GS typically achieves detection rates of over 90% (at 10% false alarm rates) over a broad range of SNR values (3-21 dB). The GS detection performance exceeds that of the cepstrum and exhibits more robustness to noise and signal processing parameters. Simulation results with fixed system effects indicate better estimation performance for cepstral-based methods, while experimental phantom results show the GS estimation performance to be the same or better than the cepstral-based method.

Analysis and classification of tissue with scatterer structure templates.

Back-scattered ultrasonic signals provide scatterer structure information. Large-scale structures, such as tissue and tumor boundaries, typically create significant amplitude differences that reveal boundaries in conventional intensity images. Small-scale structures typically result in textures observed over regions of the intensity image. This paper describes the generalized spectrum (GS) for characterizing small-scale scatterer structures and applies it to analyze scatterer structures in a class of malignant and benign breast masses. Methods are presented for scaling and normalizing the GS to reduce effects from system response, overlaying tissue, and variability from noncritical structures. Results from a limited clinical study demonstrate an application of using the GS to discriminate between benign and malignant breast masses that contain internal echoes. Sections of rf A-scans in 41 breast mass regions were taken from 26 patients. A GS analysis was applied to determine critical structural properties between a class of fibroadenoma and carcinoma masses. Classifiers designed using significant structure differences identified by the GS analysis achieved approximately 82% true-positive and 10% false-positive rates.

Mean-scatterer spacing estimates with spectral correlation.

An ultrasonic backscattered signal from material comprised of quasiperiodic scatterers exhibit redundancy over both its phase and magnitude spectra. This paper addresses the problem of estimating mean-scatterer spacing from the backscattered ultrasound signal using spectral redundancy characterized by the spectral autocorrelation (SAC) function. Mean-scatterer spacing estimates are compared for techniques that use the cepstrum and the SAC function. A -scan models consist of a collection of regular scatterers with Gamma distributed spacings embedded in diffuse scatterers with uniform distributed spacings. The model accounts for attenuation by convolving the frequency dependent scattering centers with a time-varying system response. Simulation results indicate that SAC-based estimates converge more reliably over smaller amounts of data than cepstrum-based estimates. A major reason for the performance advantage is the use of phase information by the SAC function, while the cepstrum uses a phaseless power spectral density that is directly affected by the system response and the presence of diffuse scattering (speckle). An example of estimating the mean-scatterer spacing in liver tissue also is presented.

Characterization of tissue microstructure scatterer distribution with spectral correlation.

Characterization of tissue microstructure from the backscattered ultrasound signal using the spectral autocorrelation (SAC) function provides information about the scatterer distribution in biological tissue. This paper demonstrates SAC capabilities in characterizing periodicities in A-scans due to regularity in the scatterer distribution. The A-scan is modelled as a cyclostationary signal, where the statistical parameters of the signal vary in time with single or multiple periodicities. This periodicity manifests itself as spectral peaks both in the power spectral density (PSD) and in the SAC. Periodicity in the PSD will produce a well defined dominant peak in the cepstrum, which has been used to determine the scatterer spacing. The relationship between the scatterer spacing and the spacing of the spectral peaks is established using a stochastic model of the echo-formation process from biological tissue. The distribution of the scatterers within the microstructure is modelled using a Gamma function, which offers a flexible method of simulating parametric regularity in the scatterer spacing. Simulations of the tissue microstructure for lower orders of regularity indicate that the SAC components reveal information about the scatterer spacing that are not seen in the PSD and the cepstrum. The echoformation process is tested by simulating microstructure of varying regularity and analyzing their effect on the SAC, PSD and cepstrum. Experimental validation of the simulation results are provided using in vivo scans of the breast and liver tissue that show the presence of significant spectral correlation components in the SAC.

Spectral correlation in ultrasonic pulse echo signal processing.

The effects of using spectral correlation in a maximum-likelihood estimator (MLE) for backscattered energy corresponding to coherent reflectors embedded in media of microstructure scatterers is considered. The spectral autocorrelation (SAC) function is analyzed for various scatterer configurations based on the regularity of the interspacing distance between scatterers. It is shown that increased regularity gives rise to significant spectral correlation, whereas uniform distribution of scatters throughout a resolution cell results in no significant correlation between spectral components. This implies that when a true uniform distribution for the effective scatterers exists, the power spectral density (PSD) is sufficient to characterize their echoes. However, as the microstructure scatterer distribution becomes more regular, SAC terms become more significant. MLE results for 15 A-scans from stainless steel specimens with three different grain sizes indicate an average 6-dB signal-to-noise ratio (SNR) improvement in the coherent scatterer (flat-bottom hole) echo intensities for estimators using the SAC characterization as opposed to the PSD characterization.

Maximum likelihood estimation of A-scan amplitudes for coherent targets in media of unresolvable scatterers.

The author derives a maximum-likelihood estimator (MLE) for A-scan amplitudes corresponding to coherent reflectors embedded in media of unresolvable scatterers. The MLE processes sampled RF A-scans from broadband ultrasonic pulse-echo systems. A major source of interference for these signals is the backscattered energy from the unresolvable scatterers that exist throughout the beam field. A statistical model is formulated that characterizes the backscattered energy from a resolution cell when a coherent target scatterer is present. It is shown that the MLE is equivalent to a matched filter when the distribution of the interfering back-scatter energy is stationary over the resolution cell. In addition, the form of the MLE is described when the interfering echoes are not stationary within the resolution cell. Experimental results are presented for an adaptive implementation of the MLE applied to flaw detection in stainless steel. The results demonstrate the ability of the MLE to reveal targets masked by grain echoes, without prior knowledge of the gain-echo spectral characteristics.

Analysis of order statistic filters applied to ultrasonic flaw detection using split-spectrum processing.

Split-spectrum processing of broadband ultrasonic signals coupled with order statistic filtering has proven to be effective in improving the flaw-to-clutter ratio of backscattered signals. It is shown that an optimal rank can be obtained with a prior knowledge of flaw-to-clutter ratio and the underlying distributions. The order statistic filter performs well where the flaw and clutter echoes have good statistical separation in a given quantile region representing a particular rank (e.g. minimum, median, maximum). Order statistic filters are analyzed for the situation in which the observations do not contain equivalent statistical information. Experimental and simulated results are presented to show how effectively the order statistic filter can utilize information contained in different frequency bands to improve flaw detection.